{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "70ec2a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from sympy import symbols, cos, sin\n",
    "from sympy.plotting import plot_parametric\n",
    "from math import sin,cos\n",
    "import io\n",
    "import cv2\n",
    "import pykalman\n",
    "from scipy import optimize\n",
    "import matplotlib as mpl\n",
    "from numpy import array, arange, abs as np_abs\n",
    "from numpy.fft import rfft, rfftfreq\n",
    "from math import sin, pi\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b699426d",
   "metadata": {},
   "source": [
    "# Лабораторная работа 2.1\n",
    "Проверка теоремы Котельникова на сигнале $x(t) = sin(w_0 \\cdot t) $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "31777ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w0 = 3\n",
    "f = w0/(2*math.pi)\n",
    "FD = f /2 \n",
    "r = 5\n",
    "fig1 = plt.figure(1)\n",
    "axes1 = fig1.subplots(1, 1)\n",
    "sin_sig = array([sin(w0 * t * 0.005) for t in range(int(r/0.005))])#график сигнала\n",
    "axes1.plot(array([t * 0.005 for t in range(int(r/0.005))]), sin_sig, 'r')\n",
    "axes1.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d9afa88d",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = array([sin(w0 * t * FD) for t in range(int(r/FD))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b6b29e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = []\n",
    "cc = 0.005\n",
    "for i in range(int(r/cc)):\n",
    "    ssum = 0\n",
    "    for j in range(int(r/FD)):\n",
    "        if i*cc == j*FD:\n",
    "            ssum+= s[j]\n",
    "        else:\n",
    "            ssum+= s[j] * math.sin(math.pi/FD * (i*cc  - j*FD  )) / (math.pi/FD * (i*cc - j*FD))\n",
    "    y.append(ssum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4908706d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          0.65652098  0.99043774  0.83766879  0.2732824  -0.42539071\n",
      " -0.91503344 -0.95504294 -0.52575918  0.16187448  0.76996553  0.99970748\n",
      "  0.73820871  0.11396564 -0.56627838 -0.96826199 -0.8944567  -0.38112928\n",
      "  0.31947915  0.86310048]\n",
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     ]
    }
   ],
   "source": [
    "print(s)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bb67e7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes1.plot(array([t * cc for t in range(int(r/cc))]), y, 'b')\n",
    "fig1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "620e1ba4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4c45cf6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd72a8c3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
